Signal separating device and signal separating method

ABSTRACT

A signal detection apparatus improving signal detection accuracy in a receiver for communications system complying with the MIMO scheme. The signal detection apparatus detects transmission signals sent from transmission antennas based on received signals received by receiving antennas. The apparatus includes: a first determination mechanism for determining symbol candidates of the transmission signals in sequence based on the QRM-MLD method using the received signals arranged in first order; a second determination mechanism for determining symbol candidates of the transmission signals in sequence based on the QRM-MLD method using the received signals arranged in second order that is different from the first order; and an output mechanism for outputting symbol candidates and likelihood of the transmission signals based on determination results of at least the first and the second determination mechanisms.

BACKGROUND

1. Technical Field

The present invention generally relates to a technical field of wirelesscommunications of a Multiple-Input Multiple-Output (MIMO) scheme. Moreparticularly, the present invention relates to a signal detectionapparatus and a signal detection method used in a receiver for the MIMOscheme.

2. Background Art

In this kind of technical fields research and development are beingpromoted for realizing large-capacity high-speed informationcommunications coming after current and next-generation techniques. Forexample, in addition to a Single-Input Single-Output (SISO) scheme,researches are being promoted for a Single-Input Multiple-Output (SIMO)scheme, a Multiple-Input Single-Output (MISO) and the Multiple-InputMultiple-Output (MIMO) scheme and the like from the viewpoint ofincreasing communications capacity.

FIG. 1 shows an outline of a communications system of the MIMO schemeincluding a transmitter 102 and a receiver 104. In the MIMO scheme,different signals are transmitted from transmission antennas 106-1˜Nsimultaneously with a same frequency. These transmission signals arereceived by receiving antennas 108-1˜N. Although, both of the number ofthe transmission antennas and the number of the receiving antennas are Nfor the sake of simplicity, they may be different. In the receiver 104,a process is performed for detecting each signal of a plurality ofsignals from the transmitter based on the received signals received byeach receiving antenna. The detected signals are supplied to subsequentprocess components for performing further demodulation process.

There are several methods for signal detection performed in the receiver104. One is a method called Maximum Likelihood Detection (MLD) method.In this method, a Euclidean distance or the square is calculated forevery possible combination of the transmission signals transmitted fromthe transmission antennas and the received signal so as to select acombination of transmission signals that provides a minimum distance.According to this method, each signal of the transmission signals can bedetected with reliability. But, there is a problem in that calculationload required for signal detection becomes large since it is necessaryto calculate the squared Euclidean distance many times. For example,assuming that four transmission signals are transmitted from fourtransmission antennas using a 16QAM modulation scheme. In this case,since a transmission signal is mapped into any one of 16 constellationpoints, a total sum of combinations of transmission signals included inthe received signal becomes (number of constellation points for onetransmission signal)^((number of transmission antennas))=16⁴=65536. Itrequires very large calculation capacity to calculate the squaredEuclidian distance for every combination so as to select a maximumlikelihood combination, and, especially, downsizing of mobile terminalsis inhibited. Further, when the calculation load is large, powerconsumption becomes large, which is especially disadvantageous for asmall mobile terminal.

The QRM-MLD method is a signal detection method modified from the MLDmethod. In this method, QR decomposition and M algorithm are combinedwith the MLD method so as to try to decrease the number of times ofcalculations of the squared Euclidean distance. According to thismethod, in the above-mentioned assumed example, the number of times ofthe calculation can be decreased to (number of candidates ofconstellation points in a first stage)+(number of newly added candidatesof constellation points)×(number of surviving candidates ofconstellation points in previous stage)×(number of transmissionantennas)=16+16×16×3=784. The QRM-MLD method is described in thenon-patent document 1, for example.

FIG. 2 shows a partial block diagram of a receiver that performs signaldetection according to a conventional QRM-MLD method. For the sake ofsimplicity, four transmission signals x=(x₁ . . . x₄)^(T) aretransmitted from four transmission antennas respectively with the 16 QAMmodulation scheme (the superscript T represents transpose). The receiverincludes a plurality of receiving antennas 202-1, 202-2, 202-3 and202-4, a channel estimation unit 204, a ranking unit 206, a reorderingunit 208, a QR decomposition unit 210 a signal conversion unit 212, amaximum likelihood determination unit 214, and a likelihood output unit215. The maximum likelihood determination unit 214 includes fourdetermination units 216-1, 216-2, 216-3 and 216-4. The number ofdetermination units is determined according to the number oftransmission signals. Since each determination unit includes sameprocess blocks, a fourth determination unit 216-4 is described as arepresentative. The determination unit includes a symbol replicageneration unit 218-4, a squared Euclidian distance calculation unit220-4 and a surviving symbol candidate selection unit 222-4.

The channel estimation unit 204 obtains a channel impulse response (CIR)or a channel estimation value based on a received signal including apilot signal known in both sides of transmission and receiving. A matrixH having each channel estimation value hag as each matrix element iscalled a channel matrix, wherein h_(nm) represents a channel estimationvalue between a m-th transmission antenna and a n-th receiving antenna,and, 1≦n, m≦4 holds true in the present example.

The ranking unit 206 rates or ranks a plurality of received signals y₁,. . . , y₄ in order of the size of power.

The reordering unit 208 reports arranging order of the received signalsto the QR decomposition unit 210 and to the signal conversion unit 212.

The QR decomposition unit 210 obtains matrixes Q and R such that thechannel matrix H obtained by the channel estimation unit 204 isrepresented as a product of a unitary matrix Q and an upper triangularmatrix R (H=QR).

The signal conversion unit 212 multiplies a vector y=(y₁ . . . y₄)^(T)having the received signals as its elements by a conjugate transposematrix Q^(H) of the unitary matrix Q to perform signal conversion. Thesuperscript H indicates conjugate transpose The relationship of y=Hx=QRxholds true between a transmission signal x and a received signal y. Bymultiplying this equation by Q^(H) from the left, the left side becomesQ^(H)y=z and the right side becomes Q^(H)QRx=Q⁻¹QRx=Rx. Therefore,relationship between the transmission and received signals can berepresented as z=Rx as follows, wherein z=(z₁ . . . z₄)^(T)=Q^(H)y.

The relational express on can be also written as follows.z ₁ =r ₁₁ x ₁ +r ₁₂ x ₂ +r ₁₃ x ₃ +r ₁₄ x ₄z ₂ =r ₂₂ x ₂ +r ₂₃ x ₃ +r ₂₄ x ₄z ₃ =r ₃₃ x ₃ +r ₃₄ x ₄z₄=r₄₄x₄

The maximum likelihood determination unit 214 narrows down symbolcandidates of transmission signals using the maximum likelihooddetermination method (MLD method). The symbol replica generation unit218-4 in the determination unit 216-4 generates symbol candidates oftransmission signals corresponding to a received signal x₄ using matrixelements of the upper triangular matrix R. The number of symbolcandidates is c, for example.

The squared Euclidean distance calculation unit 220-4 calculates asquared Euclidean distance between the converted received signal z_(i)and c symbol candidates. The squared Euclidian distance represents ametric on which calculation of likelihood is based. A symbol candidatefor which small squared Euclidian distance is obtained is determined tobe one near a transmitted symbol.

The surviving symbol candidate selection unit 222-4 outputs S₁(≦C)symbol candidates as surviving symbol candidates based on the squaredEuclidian distance for each candidate.

The likelihood output unit 215 calculates likelihood or reliability ofthe symbol candidates output from the surviving symbol candidate unit ofthe final stage. More particularly, the likelihood is represented as LLR(Log Likelihood Ratio). The output from the likelihood output unit 215represents a signal detection result and is transmitted to a modulationunit (turbo decoder, for example) of a subsequent stage.

Operation is described next. The receiver receives transmission signalsas received signals y₁˜y₄ with four antennas. These signals are suppliedto the channel estimation unit 204 and the signal conversion unit 212.The order of the received signals are determined by the channelestimation unit 204, the ranking unit 206 and the reordering unit 208.In this example, the received signals are ordered in order of the sizeof received powers and it is assumed that received power becomes largerin order of x₁, x₂, x₃ and x₄. The received signals are converted suchthat z=(z₁ . . . z₄)^(T)=Q^(H)y holds true by the signal conversion unit212, and the converted signals are supplied to the maximum likelihooddetermination unit 214.

In a first stage in the maximum likelihood determination unit 214, aprocess corresponding to initial setting is performed in thedetermination unit 216-4. In this stage, the above equation on z₄ isfocused on. Since a matrix element r₄₄ is known, it turns out that z₄depends only on one transmission signal x₄. Therefore, the transmissionsignal x₄ has 16 constellation point candidates at most. The symbolcandidate generation unit 218-4 generates 16 (C=16) symbol candidates onx₄. The squared Euclidian distance calculation unit 220-4 calculatessquared Euclidian distances between each symbol candidate and the fourthreceived signal z₄. Then, S₁ symbol candidates are selected in ascendingorder of the distance as surviving symbol candidates.

A second stage is performed by the determination unit 216-3. In thisstage, the equation on z₃ is focused on. Matrix elements r₃₃ and r₃₄ areknown, there are 16 candidates for x₄, and also there are 16constellation candidates for x₃. As new constellation points on x₃, 16constellation points are introduced by the symbol generation unit 218-3.Therefore, there may be 16×16=256 combinations of constellation points.Thus, 256 squared Euclidian distances between each of these symbolcandidates and the third received signal x₃ are calculated, so thatsymbol candidates are narrowed down by selecting 16 (S₂=16) candidatesin ascending order of the value.

In a third stage, similar process is performed in the determination unit216-2. In this stage, the equation on z₂ is focused on. Matrix elementsr₂₂, r₂₃ and r₂₄ are known, combinations of transmission signals x₃ andx₄ are narrowed down to 16 candidates in the previous stage, and thereare 16 constellation point candidates for x₂. Therefore, the symbolcandidate generation unit 218-2 generates 16 symbol candidates on x₂.Also in this case, 16 (S₃=16, candidates having small squared Euclidiandistance are selected from among 256 constellation point combinations soas to narrow down symbol candidates.

In a fourth stage, similar process is performed in the determinationunit 216-1. In this stage, the equation on z₁ is focused on. Matrixelements r₁₁, r₁₂, r₁₃ and r₁₄ are known, combinations of transmissionsignals x₂, x₃ and x₄ are narrowed down to 16 candidates in the previousstage, and there are 16 constellation point candidates for x₁.Therefore, the symbol candidate generation unit 218-1 generates 16symbol candidates on x₁. In the fourth stage, 256 combinations ofconstellation points are output to the likelihood output unit 215.

Accordingly, by limiting the number of symbol candidates to equal to orless than a constant number (S₁≦C and the like) in each stage, symbolcandidates of transmission signals can be narrowed down withoutcalculating squared Euclidian distances for all possible combinations ofconstellation points.

[Non-patent document 1] K. J. Kim, et al., “Joint channel estimation anddata detection algorithms for MIMO-OFDM systems”, Proc. 36th AsilomarConference on Signals, Systems and Computers, November 2002

PROBLEM TO BE SOLVED BY THE INVENTION

By the way, according to the above-mentioned method, the number ofsymbol candidates gradually decreases as the stage proceeds from thefirst stage to the fourth stage. For example, the number of candidatesfor the fourth transmission signal x₄ introduced in the first stagedecreases (is narrowed down) as the stage proceeds to the second state,the third stage and the fourth stage. Similarly, the number ofcandidates for the third transmission signal x₃ introduced in the secondstage decreases as the stage proceeds to the third stage and the fourthstage. The number of candidates for the second transmission signal x₂introduced in the third stage decreases as the stage proceeds to thefourth stage. Then, candidates for the first transmission signal x₁introduced in the fourth stage are reflected as output from the maximumlikelihood determination unit as it is.

FIG. 3 shows a manner in which the number of symbol candidates graduallydecreases as the stage proceeds. The size of each circle drawn above thetransmission signals x₁, x₂, x₃ and x₄ corresponds to the number ofcandidates in each stage. It turns out that the number of symbolcandidates on the fourth transmission signal x₄ is smallest in thefourth stage, and that the number of symbol candidates becomes smallerin order of first, second and third transmission signals x₁, x₂ and x₃.Therefore, it is concerned that candidates for the fourth transmissionsignal x₄ may be excessively narrowed down so that an actual symbolcandidate of the fourth transmission signal x₄ may be missed in thefourth stage. As to candidates for the third transmission signal x₃,though not to the extent of the transmission signal x₄/it is concernedthat candidates may be excessively narrowed down so that an actualsymbol candidate for the third transmission signal x₃ may be missed inthe fourth stage in the example shown in the figure, each symbolcandidate number for transmission signals x₄ and x₃ enclosed by a dottedline frame becomes very small. If an actual symbol candidate is missed,estimation accuracy for the transmission signal, that is, transmissioncharacteristics degrade. Such excessive narrowing down may be avoided tosome extent by dealing with a signal having high power value as atransmission signal determined in the first stage. But, that is notenough. Excessive narrowing down for symbol candidates may be avoided byincreasing the number of surviving symbol candidates in each stage.However, in exchange for that, calculation load for calculating thesquared Euclidian distances largely increases.

FIG. 4 shows a simulation result in which signal detection for the firstto fourth transmission signals x₁, x₂, x₃ and x₄ is performed on thebasis of the above-mentioned assumed example. Following are mainconditions used for the simulation:

Number of transmission and receiving antennas: 4;

Modulation scheme: 16QAM;

Turbo coding ratio: 8/9;

Estimated number of multi-paths L: 6;

Delay spread σ: 0.26 μsec

In the figure, the lateral axis indicates an average of signal power tonoise power density ratio per 1 bit of information (Eb/N₀). The verticalaxis indicates average packet error rate (PER). Signs of ▴, ♦, ▪ and ●indicate estimation results for transmission signals x₄, x₃, x₂ and x₁respectively. As shown in the figure, it turns out that estimationaccuracy for transmission signal x₄ (▴) where the number of symbolcandidates is smallest is worst, and that estimation accuracy fortransmission signal x₁(●) where the number of symbol candidates is keptto be largest is the best. As to the transmission signals x₂ and x₃,similar tendency can be found. Accordingly, as to signal detectionmethod by the conventional QRM-MLD method, it is concerned thatestimation accuracy for a part of transmission signals (the whole of thetransmission signals by extension) degrade. In addition, according tothe conventional signal detection method, it is also inconvenient thatquality of signal estimation accuracy varies among a plurality oftransmission signals.

The present invention is contrived for solving at least one of theabove-mentioned problems, and an object is to provide a signal detectionapparatus and a signal detection method for improving signal detectionaccuracy in a receiver for a communications system complying with theMIMO scheme.

SUMMARY

In the present invention, a signal detection apparatus for detectingtransmission signals from transmission antennas based on receivedsignals received by receiving antennas is used. The apparatus includes:

first determination means for determining symbol candidates of thetransmission signals based on the QRM-MLD method using the receivedsignals arranged in first order;

second determination means for determining symbol candidates of thetransmission signals based on the QRM-MLD method using the receivedsignals arranged in second order that is different from the first order;and

output means for outputting symbol candidates and likelihood of thetransmission signals based on determination results of at least thefirst and the second determination means.

EFFECT OF THE INVENTION

According to the present invention, signal detection accuracy can beimproved in a receiver for the communications system complying with theMIMO scheme.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a figure showing an outline of a communications system of theMIMO scheme;

FIG. 2 is a partial block diagram of a receiver that performsconventional signal detection;

FIG. 3 is a schematic diagram showing a manner for narrowing down symbolcandidates;

FIG. 4 is a figure showing a simulation result according to aconventional signal detection method;

FIG. 5 is a partial block diagram of a receiver for performing signaldetection according to an embodiment of the present invention;

FIG. 6 is a flowchart indicating a signal detection method according toan embodiment of the present invention;

FIG. 7 is a schematic diagram showing a manner in which each maximumlikelihood determination unit narrows down symbol candidatesindependently;

FIG. 8 is a schematic diagram showing a manner in which one of maximumlikelihood determination units narrows down symbol candidates inconjunction with another one.

DETAILED DESCRIPTION Description of Reference Signs

102 transmitter; 104 receiver; 106-1-N transmission antenna; 108-1˜Nreceiving antenna; 202-1˜4 receiving antenna; 204 channel estimationunit; 206 ranking unit; 208 reordering unit; 210 QR decomposition unit;212 signal conversion unit; 214 maximum likelihood determination unit;215 likelihood output unit; 216-1˜4 determination unit; 218-1˜4 symbolreplica generation unit; 220-1˜4 squared Euclidian distance calculationunit; 222-1˜4 surviving symbol candidate selection unit;

502-1˜4 receiving antenna; 504 channel estimation unit; 506 rankingunit; 508-1,2 reordering unit; 510-1,2 QR decomposition unit; 512-1,2signal conversion unit; 514-1,2 maximum likelihood determination unit;516 symbol candidate selection unit; 518 likelihood output unit; 520signal line

According to an embodiment of the present invention, the receivedsignals are arranged in first order, symbol candidates of thetransmission signals are determined based on the QRM-MLD methodaccording to the first order, the received signals are arranged insecond order that is different from the first order, symbol candidatesof the transmission signals are determined based on the QRM-MLD methodaccording to the second order, and symbol candidates and likelihood ofthe transmission signals are output based on determination resultsobtained with respect to the first and the second orders. Accordingly,there occurs a possibility that a symbol candidate missed in one maximumlikelihood determination is not missed in another maximum likelihooddetermination, so that signal estimation accuracy can be improved byconsidering both of determination results. Therefore, estimationaccuracy can be improved without increasing calculation loadexcessively. In addition, variations of estimation accuracy amongtransmission symbols can be decreased to be small.

According to an embodiment of the present invention, the second order isthe reverse of the first order. Accordingly, a symbol candidate missedin one maximum likelihood determination can be kept in another maximumlikelihood determination with high possibility.

According to an embodiment of the present invention, the seconddetermination means determines symbol candidates of a part of thetransmission signals according to the detection result of the firstdetermination means, and determines symbol candidates of remainingtransmission signals using the QRM-MLD method. Accordingly, calculationload in the second determination unit can be decreased.

According to an embodiment of the present invention, the output meansupdates likelihood of a symbol candidate commonly included in detectionresults of the first and the second determination means. For example,the updated likelihood is a weighted average of likelihoods of symbolcandidates obtained by the first and the second determination means.Also for example, the updated likelihood is less than any of likelihoodsof symbol candidates obtained by the first and the second determinationmeans. Also for example, the updated likelihood is equal to one oflikelihoods of symbol candidates obtained by the first and the seconddetermination means. Accordingly, likelihood of the symbol candidatecommonly included in both of the determination results can be evaluatedmore properly.

In the following, embodiments of the present invention are described.

First Embodiment

FIG. 5 shows a partial block diagram of a receiver for performing thesignal detection method according to an embodiment of the presentinvention. For the sake of simplicity, it is assumed that fourtransmission signals x=(x₁ x₄)^(T) are transmitted from the fourtransmission antennas respectively with the 16QAM modulation scheme.But, the number of antennas and the modulation scheme and the like arenot limited to these. The receiver includes a plurality of receivingantennas 502-1, 502-2, 502-3 and 502-4, a channel estimation unit 504, aranking unit 506, reordering units 508-1 and 2, OR decomposition units510-1 and 2, signal conversion units 512-1 and 2, maximum likelihooddetermination units 514-1 and 2, symbol candidate selection unit 516 anda likelihood output unit 518. Since each of the maximum likelihooddetermination units 514-1 and 2 includes configuration and functionssimilar to those of the maximum likelihood determination unit 214 shownin FIG. 2, detailed description is not provide.

The channel estimation unit 504 obtains channel impulse response (CIR)or channel estimation value based on received signals including a knownpilot signal in both sides of transmission and receiving. A matrix Hincluding each channel estimation value hag as each matrix element iscalled a channel matrix, wherein h_(nm) represents a channel estimationvalue between a m-th transmission antenna and a n-th receiving antenna,and 1≦n, m≦4 is satisfied in this example. But, various values may betaken as the number of antennas.

The ranking unit 506 ranks the received transmission signals x₁, . . . ,x₄ in order of signal quality. The signal quality is evaluated accordingto size of received signal power, ratio of desired wave power ornon-desired wave power to total received power and the like.

The reordering unit 508-1 arranges the received signals in first orderaccording to the ranks assigned to each received signal by the rankingunit 506, and reports the order to the QR decomposition unit 510-1 andto the signal conversion unit 512-1. In this embodiment, the first orderis an order in which received signal power of each received signalincreases gradually. For the sake of simplicity, it is assumed that thefirst order is y₁, y₂, y₃ and y₄.

The reordering unit 508-2 arranges the received signals in second orderaccording to the ranks assigned to each received signal by the rankingunit 506 and reports the order to the QR decomposing unit 510-2 and tothe signal conversion unit 512-2. In this embodiment, the second orderis an order in which received signal power of each received signaldecreases gradually, namely it is reversal of the first order Receivedsignals are arranged in the second order as y₄, y₃, y₂ and y₁.

The QR decomposition unit 510-1 obtains matrixes Q and R such that thechannel matrix H obtained by the channel estimation unit 504 isrepresented as a product of a unitary matrix Q and an upper triangularmatrix R (H=QR). Information on the matrix Q is provided to the signalconversion unit 512-1, and information on the matrix R is provided tothe maximum likelihood determination unit 514-1.

The QR decomposition unit 510-2 obtains matrixes Q′ and R′ such that thechannel matrix H′ obtained by the channel estimation unit 504 isrepresented as a product of a unitary matrix Q′ and an upper triangularmatrix R′ (H′=Q′R′). Information on the matrix Q′ is provided to thesignal conversion unit 512-2, and information on the matrix R′ isprovided to the maximum likelihood determination unit 514-2. Thereceived signals are supplied to the QR decomposition units 510-1 and 2in different orders by the reordering units 508-1 and 2 respectively.The channel matrix varies depending on the order of the receivedsignals. Thus, it is general that unitary matrixes and upper triangularmatrixes calculated by the QR decomposition units 510-1 and 2 aredifferent.

The signal conversion unit 512-1 performs signal conversion (z=Q^(H)y)by multiplying a vector y=(y₁ y₂ y₃ y₄)^(T) including the receivedsignals as components by a conjugate transpose matrix Q^(H) of theunitary matrix Q. Between the received signal z after signal conversionand transmission signal x, z=Rx holds true.

$\begin{bmatrix}z_{1} \\z_{2} \\z_{3} \\z_{4}\end{bmatrix} = {\begin{bmatrix}r_{11} & r_{12} & r_{13} & r_{14} \\0 & r_{22} & r_{23} & r_{24} \\0 & 0 & r_{33} & r_{34} \\0 & 0 & 0 & r_{44}\end{bmatrix}\begin{bmatrix}x_{1} \\x_{2} \\x_{3} \\x_{4}\end{bmatrix}}$

The above relational expression can be written as:z ₁ =r ₁₁ x ₁ +r ₁₂ x ₂ +r ₁₃ x ₃ +r ₁₄ x ₄z ₂ =r ₂₂ x ₂ +r ₂₃ x ₃ +r ₂₄ x ₄z ₃ =r ₃₃ x ₃ +r ₃₄ x ₄z₄=r₄₄x₄.

The signal conversion unit 512-2 performs signal conversion(z′=Q′^(H)y′) by multiplying a vector y′=(y₁ y₂ y₃ y₄)^(T) including thereceived signals as components by a conjugate transpose matrix Q^(H) ofthe unitary matrix Q. Between the received signal z after signalconversion and transmission signal x, z=Rx holds true.

$\begin{bmatrix}z_{1}^{\prime} \\z_{2}^{\prime} \\z_{3}^{\prime} \\z_{4}^{\prime}\end{bmatrix} = {\begin{bmatrix}r_{11}^{\prime} & r_{12}^{\prime} & r_{13}^{\prime} & r_{14}^{\prime} \\0 & r_{22}^{\prime} & r_{23}^{\prime} & r_{24}^{\prime} \\0 & 0 & r_{33}^{\prime} & r_{34}^{\prime} \\0 & 0 & 0 & r_{44}^{\prime}\end{bmatrix}\begin{bmatrix}x_{4} \\x_{3} \\x_{2} \\x_{1}\end{bmatrix}}$

The above relational expression can be written as:

z ₁ ′=r ₁₁ ′x ₄ +r ₁₂ ′x ₃ +r ₁₃ ′x ₂ +r ₁₄ ′x ₁z ₂ ′=r ₂₂ ′x ₃ +r ₂₃ ′x ₂ +r ₂₄ ′x ₁z ₃ ′=r ₃₃ ′x ₂ +r ₃₄ ′x ₁z₄′=r₄₄′x₁.

The maximum likelihood determination unit 514-1 narrows down symbolcandidate of transmission signals using the maximum likelihooddetermination method (MLD method). In the same way as the maximumlikelihood determination unit 214 shown in FIG. 2, the maximumlikelihood determination unit 514-1 determines symbol candidates inorder of transmission signals x₄, x₃, x₂ and x₁ so as to output adetermination result. The determination result is represented bydetermined symbol candidates S_(1,i) and metrics e_(1,i) for thecandidates. The subscript “1” indicates that it relates to the firstorder. The subscript “i” is an index indicating which is selected fromamong many symbol candidates. The metric can be represented by a squaredEuclidian distances for example.

Also, the maximum likelihood determination unit 514-2 narrows downsymbol candidates of transmission signals using the maximum likelihooddetermination method (MLD method). Different from the maximum likelihooddetermination unit 514-1, the maximum likelihood determination unit514-2 determines symbol candidates in order of transmission signals x₁,x₂, x₃ and x₄ so as to output a determination result. The determinationresult is represented by determined symbol candidates S_(2,j) andmetrics e_(2,j) for the candidates The subscript “2” indicates that itrelates to the second order. The subscript “j” is an index indicatingwhich is selected from among many symbol candidates.

The symbol candidate selection unit 516 outputs one or more symbolcandidate S′_(m) with a metric e′_(m) as a maximum likelihooddetermination result based on outputs from the maximum likelihooddetermination units 514-1 and 2. There are various methods for derivingthe final determination result from the determination results based onthe first and second orders. For example, it is assumed that first andsecond determination results commonly include m-th symbol candidate andit is output as symbol candidates S_(1,m) and S_(2,m) and metricse_(1,m) and e_(2,m) from the maximum likelihood determination units514-1 and 2 respectively. In this case, the symbol candidate selectionunit 516 determines the m-th symbol candidate to be the symbol candidateS′_(m) output from it. The metric e′_(m) output from the symbolcandidate selection unit 516 may be an average value (e_(1,m)+e_(2,m))/2of the metrics, may be a value (e_(1,m)+e_(2,m))/(2X) (X>1) obtained bycontracting the average values may be a weighted average value(c₁×e_(1,m)+c₂×e_(2,m)) (c₁, c₂ are weighting coefficients) or may be asmaller value min{e_(1,m), e_(2,m)}, a value min{e_(1,m),e_(2,m)}/X(X>1) contracted from the small value, or may be a small fixed value orthe like. Or, a metric of a symbol candidate which is not commonlyincluded in the first and second determination results in symbolcandidates output from the symbol candidate selection unit 516 may beincreased to be larger than that of the symbol candidate commonlyincluded.

The likelihood output unit 518 calculates likelihood of the symbolcandidate output from the symbol candidate selection unit 516. Moreparticularly the likelihood is represented as log likelihood ratio(LLR). The output from the likelihood output unit 518 indicates a signaldetection result, and is transmitted to a later-stage modulation unitsuch as a turbo decoder, for example.

FIG. 6 is a flowchart indicating a signal detection method according toan embodiment of the present invention. The flow starts from step 602and goes to step 604. In step 604, the received signals are reordered inthe first order. The first order is an order of size of received signalpower. The reordering is performed in the ranking unit 506 and thereordering unit 508-1 shown in FIG. 5.

In step 606, symbol candidates of the transmission signals are obtainedbased the QRM-MLD method using the received signals ordered in the firstorder.

In step 608, the received signals are reordered in the second order. Thesecond order is an order reversed from the first order, for example. Thereordering is performed in the ranking unit 506 and the reordering unit508-2 shown in FIG. 5.

In step 610, symbol candidates of the transmission signals are obtainedbased the QRM-MLD method using the received signals ordered in thesecond order.

In step 612, a symbol candidate and a metric are output as the maximumlikelihood determination result based on the symbol candidate obtainedin steps 606 and 610.

After that, the flow proceeds to step 614 and the process terminates.For the sake of convenience of explanation, although step 608 ofreordering and step 610 of maximum likelihood determination by thesecond order are performed after step 606 of maximum likelihooddetermination by the first order, this is not essential for the presentinvention, and order of steps may be reversed, or the whole or a part ofsteps may be performed simultaneously.

FIG. 7 is a figure for explaining the process for narrowing down symbolcandidates sequentially. Maximum likelihood determination based on thefirst order is shown in the upper part of the figure, and maximumlikelihood determination based on the second order is shown in the lowerpart. The size of each circle shown associated with transmission signalsx₁˜x₄ corresponds to the number of symbol candidates. As shown in theupper part, symbol candidates for transmission signals x₄, x₃ and x₂decrease as the stage proceeds. Numbers of candidates of transmissionsignals included in the symbol candidate S_(1,i) output in the fourthstage that is the final stage are decreased in the order of x₁, x₂, x₃and x₄. Therefore, there is a risk that likelihood for transmissionsignals x₄ and x₃ determined in early stages such as first and secondstages becomes excessively small likelihood for transmission signals x₁and x₂ is relatively large).

However, in the present embodiments as shown in the lower part, maximumlikelihood determination is also performed based on received signalsarranged in the second order that is different from the first order. Inthe maximum likelihood determination based on the second order, symbolcandidates for transmission signals x₁, x₂ and x₃ decreases as the stageproceeds. Numbers of candidates of transmission signals included in thesymbol candidate S_(2,i) output in the fourth stage that is the finalstage are decreased in the order of x₄, x₃, x₂ and x₁.

Therefore, although there is a risk that likelihood for transmissionsignals x₁ and x₂ determined in early stages such as first and secondstages becomes excessively small, likelihood for transmission signals x₃and x₄ can be kept to be large. Therefore, even if the number of symbolcandidates for the transmission signals x₃ and x₄ becomes excessivelysmall in maximum likelihood determination based on the first ordersimproper narrowing down can be compensated for by considering themaximum likelihood determination result based on the second order.

In the present embodiment, although the first and second orders that arereversed with each other with respect to the order of the size of thereceived power are used for the sake of simplifying explanations, theorder may be determined based on other criteria. For example, an orderfollowing ratios of desired wave signal power to total power (SIR andthe like). In addition, the second order may not be arranged to bestrictly reversal to the first order. For example, the second order maybe set such that order of a part of signals in the first order ischanged (by permuting positions of x₁ and x₄ for example). The reason isthat a symbol candidate missed in a maximum likelihood determinationunit may be kept in another maximum likelihood determination unit whenthe first and second orders are different. But, like the presentembodiment, when the first and second orders are reversed, a symbolcandidate missed in the maximum likelihood determination unit 514-1 forthe transmission signal x₄ can be acquired in the maximum likelihooddetermination unit 514-2 with maximum probability. Further, although thefirst and second orders are used in the present embodiment, more thantwo orders may be set so as to perform maximum likelihood determinationrelated to the orders and select final symbol candidates from thedetermination results. By increasing kinds of orders for arrangingreceived signals, possibility for avoiding excessive narrowing down forsymbol candidates can be kept to be high.

Second Embodiment

Although maximum likelihood determination on the first order isperformed completely independently of maximum likelihood determinationon the second order in the first embodiment, a part of determinationresults of one side may be incorporated in determination calculation inanother side. For example, determination results on the first and secondtransmission signals x₁ and x₂ in maximum likelihood determinationresults on the first order shown in FIG. 5 may be used for maximumlikelihood determination on the second order. Information is providedfrom the maximum likelihood determination unit 514-1 to the maximumlikelihood determination unit 514-2 via a signal line 520.

As shown in FIG. 8, symbol candidates (enclosed by a dotted line framesof the first and second transmission signals x₁ and x₂ included indetermination results S_(1,i) based on the first order are used formaximum likelihood determination based on the second order. In theexample shown in the figures instead of performing all of the first tofourth stages, it is only necessary to perform the third and fourthstages. Since calculation for the first and second stages in maximumlikelihood determination based on the second order can be omitted,calculation load can be decreased by that. Therefore, the presentembodiment is especially advantageous when reliability of the maximumlikelihood determination result in the maximum likelihood determinationunit 514-1 (on the first and second transmission signals x₁ and x₂).

Although preferred embodiments of the present invention are described,the present invention is not limited to the embodiments, and variationsand modifications may be made without departing from the scope of theinvention.

1. A signal detection apparatus for detecting transmission signals sentfrom transmission antennas based on received signals received byreceiving antennas, comprising: a first reordering unit configured toarrange the received signals in a first order; a first QR decompositionunit configured to decompose a channel matrix between the receivedsignals and the transmission signals into a unitary matrix Q and anupper triangular matrix R that correspond to the first order; a firstdetermination unit configured to determine symbol candidates of thetransmission signals based on a QRM-MLD (Maximum Likelihood Detectionwith QR decomposition and M-algorithm) method using the received signalsarranged in the first order, and using the unitary matrix Q and theupper triangular matrix R that correspond to the first order; a secondreordering unit configured to arrange the received signals in a secondorder that is different from the first order; a second QR decompositionunit configured to decompose the channel matrix into a unitary matrix Qand an upper triangular matrix R that correspond to the second order; asecond determination unit configured to determine symbol candidates ofthe transmission signals based on the QRM-MLD method using the receivedsignals arranged in the second order, and using the unitary matrix Q andthe upper triangular matrix R that correspond to the second order; andan output unit configured to output symbol candidates and likelihood ofthe transmission signals based on determination results of at least thefirst and the second determination units.
 2. The signal detectionapparatus as claimed in claim 1, wherein the second order is the reverseof the first order.
 3. The signal detection apparatus as claimed inclaim 1, wherein the first or the second order is an order based on asignal quality of the received signals.
 4. The signal detectionapparatus as claimed in claim 1, wherein the second determination unitdetermines symbol candidates of a part of the transmission signalsaccording to the detection result of the first determination unit. 5.The signal detection apparatus as claimed in claim 1, wherein the outputunit updates likelihood of a symbol candidate commonly included indetection results of the first and the second determination units. 6.The signal detection apparatus as claimed in claim 5, wherein theupdated likelihood is a weighted average of likelihoods of symbolcandidates obtained by the first and the second determination units. 7.The signal detection apparatus as claimed in claim 5, wherein theupdated likelihood is less than any of likelihoods of symbol candidatesobtained by the first and the second determination units.
 8. The signaldetection apparatus as claimed in claim 5, wherein the updatedlikelihood is equal to one of likelihoods of symbol candidates obtainedby the first and the second determination units.
 9. A signal detectionmethod, implemented on a signal detection apparatus, for detectingtransmission signals sent from transmission antennas based on receivedsignals received by receiving antennas, comprising: arranging, at afirst reordering unit of the signal detection apparatus, the receivedsignals in a first order; decomposing, at a first QR decomposition unitof the signal detection apparatus, a channel matrix between the receivedsignals and the transmission signals into a unitary matrix Q and anupper triangular matrix R that correspond to the first order;determining, at a first determination unit of the signal detectionapparatus, symbol candidates of the transmission signals based on aQRM-MLD (Maximum Likelihood Detection with QR decomposition andM-algorithm) method using the received signals arranged in the firstorder, and using the unitary matrix Q and the upper triangular matrix Rthat correspond to the first order; arranging, at a second reorderingunit of the signal detection apparatus, the received signals in a secondorder that is different from the first order; decomposing, at a secondQR decomposition unit of the signal detection apparatus, the channelmatrix into a unitary matrix Q and an upper triangular matrix R thatcorrespond to the second order; determining, at a second QRdetermination unit of the signal detection apparatus, symbol candidatesof the transmission signals based on the QRM-MLD method using thereceived signals arranged in the second order, and using the unitarymatrix Q and the upper triangular matrix R that correspond to the secondorder; and outputting, at an output unit of the signal detectionapparatus, symbol candidates and likelihood of the transmission signalsbased on determination results obtained with respect to at least thefirst and the second orders.